---
title: "Coronavirus in Sri Lanka"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus")
library(coronavirus)
data(coronavirus)
# update_datasets()
# View(coronavirus)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Sri Lanka") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  dplyr::filter(Country.Region == "Sri Lanka") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))

dfSL <- data.frame(corona_SL <- data.frame(date = seq(as.Date("2020-1-27"), as.Date("2020-3-24"), by="days"),
  "confirmed" = c(1, rep(0, 43), 1L, 2L,2L, 5L, 8L, 10L, 13L, 11L, 13L, 6L,                                     6L, 9L, 10L, 3L),
  death = rep(0, 58),
  cumsum = c(rep(1, 44), 2, 4, 6, 11, 19, 29, 42, 53, 66, 72, 78, 87, 97, 100)
  
  
))
```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(100, big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### active {.value-box}

```{r}
valueBox(
  value = paste(format(229, big.mark = ","), 
    
    sep = ""
  ),
  caption = "Total number of individuals in hospitals", icon = "fas fa-ambulance",
  color = active_color
)
```

### Recoverd {.value-box}

```{r}

valueBox(
  value = paste(format(2, big.mark = ","), 

    sep = ""
  ),
  caption = "Recovered",
  icon = "fas fa-home",
  color = recovered_color
)
```

### Deaths {.value-box}

```{r}

valueBox(
  value = paste(format(0, big.mark = ","), 

    sep = ""
  ),
  caption = "Deaths",
  icon = "fas fa-heart-broken",
  color = death_color
)
```

Column {data-width=400}
-------------------------------------




### **Cummulative count** (Sri Lanka only)


    
```{r}
plotly::plot_ly(data = dfSL) %>%
  plotly::add_trace(
    x = ~date,
    y = ~cumsum,
    type = "scatter",
    mode = "lines+markers",
    name = "New cases",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative count"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )

dflocation <- data.frame(location=c("Colombo", "Kalutara", "Gampaha", "Puttalam", "Ratnapura", "Kurunegala", "Galle", "Kegalle", "Batticoloa", "Badulla", "Jaffna", "Matara", "Kandy", "Matale", "Nuwara Eliya", "Hambantota", "Kilinochchi", "Mannar", "Vavunia", "Mullativu", "Ampara", "Trincomalee", "Anuradhapura", "Polonnaruwa", "Moneragala", "Kalmunai", "Quarantine Centres", "Foreigners"),
date24march=c(21, 13, 10, 8, 3, 1, 1, 1, 1, 1, 1, 1, rep(0, 14), 32, 3))
```


### **Daily new cases** (Sri Lanka only)


    
```{r}
plotly::plot_ly(data = dfSL) %>%
  plotly::add_bars(
    x = ~date,
    y = ~confirmed,
    type = "scatter",
    mode = "lines+markers",
    name = "New cases",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-01-27"),
    y = 0.5,
    text = paste("Foreign: Wuhan, China"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 100,
    ay = -70
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-11"),
    y = 1,
    text = paste("First Sri Lankan (Origin: Italy)"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "New patients"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

### **Distribution of Confirmed Patients**

Comparison
=======================================================================


Column {data-width=400}
-------------------------------------


### **Daily new cases**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(cases >= 2) %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

#----------------------------------------
# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~ "Sri Lanka",
    type = "scatter",
    mode = "lines+markers",
    name = "Sri Lanka"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~France,
    type = "scatter",
    mode = "lines+markers",
    name = "France"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Spain"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Italy"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```
 
### **Cases distribution by type**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Sri Lanka" |
    Country.Region == "France" |
    Country.Region == "Italy" |
    Country.Region == "Spain") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  y = ~unrecovered,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Active",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Map
=======================================================================

### **World map of cases** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(Country.Region == "Belgium") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~Long, lat = ~Lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





About
=======================================================================

**Created by**

[Thiyanga Talagala](https://thiyanga.netlify.com/)


This Coronavirus dashboard: the case of Sri Lanka provides an overview of the Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Sri Lanka. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.


**Code**

The code behind this dashboard is available on [GitHub](https://github.com/thiyangt/CoronaSriLanka){target="_blank"}.


**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.


**Data**

The input data for this dashboard is the dataset available from the {coronavirus} R package. 

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.


**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.


*Go back to [www.statsandr.com](https://www.statsandr.com/) (blog) or [www.antoinesoetewey.com](https://www.antoinesoetewey.com/) (personal website)*.